Supplementary material for “Uprooting sub-standard visualisation practices for decision-making in operational management”

Department of Econometrics and Business Statistics, Monash University, Melbourne, VIC 3800
emi.tanaka@monash.edu

Department of Econometrics and Business Statistics, Monash University, Melbourne, VIC 3800
jessica.leung@monash.edu

Department of Econometrics and Business Statistics, Monash University, Melbourne, VIC 3800
dicook@monash.edu

This is the supplementary material to an invited commentary for Basole et al. (2021). We provide all code that are used to generate the figures in the commentary in addition to other supplementary figures (and its code). To see the code, click on the CODE button. You can also download the whole R Markdown file from the drop down menu on the top right corner.

List of figures

• Figure S1: Recreating Figure 3 of Basole et al. (2021) using ggplot2.
• Figure S2: An alternative design to Figure 3 of Basole et al. (2021).
• Figure S3 Lineup for the tile grid plot used in Figure 3 of Basole et al. (2021).
• Figure S4 Lineup for the scatter plot.
• Figure S5 Lineup for the tile grid plot on data with purposely high association.
• Figure S6 Lineup for the scatter plot on data with purposely high association.

Code to load library and data

library(tidyverse)
library(ggtext)
library(patchwork)
library(readxl)
library(nullabor)
library(here)
library(janitor)
library(scales)
#theme_set(theme_classic())
knitr::opts_chunk$set(fig.path = "images/",
                      dev = c("png", "pdf", "svg"),
                      cache = TRUE,
                      cache.path = "cache/")

df_full <- read_xlsx(here("data/MaskedCoverage-Fig3.xlsx")) %>% 
  clean_names() %>% 
  add_row(state = c("OR", "WY", "SD", "WV", "DC", "AL")) %>% 
  mutate(row = case_when(
    state %in% c("ME") ~ 1L,
    state %in% c("VT", "NH") ~ 2L,
    state %in% c("WA", "ID", "MT", "ND", "MN", "IL", "WI", "MI", "NY", "RI", "MA") ~ 3L,
    state %in% c("OR", "NV", "WY", "SD", "IA", "IN", "OH", "PA", "NJ", "CT") ~ 4L,
    state %in% c("CA", "UT", "CO", "NE", "MO", "KY", "WV", "VA", "MD", "DE") ~ 5L,
    state %in% c("AZ", "NM", "KS", "AR", "TN", "NC", "SC", "DC") ~ 6L,
    state %in% c("OK", "LA", "MS", "AL", "GA") ~ 7L,
    state %in% c("TX", "FL") ~ 8L,
                         TRUE ~ 0L),
    col = case_when(
      state %in% c("WA", "OR", "CA") ~ 1L,
      state %in% c("ID", "NV", "UT", "AZ") ~ 2L,
      state %in% c("MT", "WY", "CO", "NM") ~ 3L,
      state %in% c("ND", "SD", "NE", "KS", "OK", "TX") ~ 4L,
      state %in% c("MN", "IA", "MO", "AR", "LA") ~ 5L,
      state %in% c("IL", "IN", "KY", "TN", "MS") ~ 6L,
      state %in% c("WI", "OH", "WV", "NC", "AL") ~ 7L,
      state %in% c("MI", "PA", "VA", "SC", "GA") ~ 8L,
      state %in% c("NY", "NJ", "MD", "DC", "FL") ~ 9L,
      state %in% c("VT", "RI", "CT", "DE") ~ 10L,
      state %in% c("ME", "NH", "MA") ~ 11L,
      TRUE ~ 0L
    ))

df_miss <- df_full %>% 
  filter(!is.na(readmission_rate))

g1 <- ggplot(df_miss, aes(col, row)) +
  geom_point(aes(size = coverage_obscured, color = readmission_rate * 100), alpha = 0.8) +
  geom_text(data = df_full, aes(label = state), color = "black", nudge_y = 0.05) +
  geom_text(aes(label = percent(readmission_rate, 0.01)), nudge_y = -0.1, size = 2.5) +
  theme_void() +
  scale_color_gradient2(low = "#3F6E9A", high = "#AB4C30", midpoint = median(df_miss$readmission_rate * 100), mid = "#E7D9C6") +
  scale_size(range = c(3, 30)) +
  scale_y_reverse() +
  theme(plot.margin = margin(r = 30)) +
  labs(color = "Readmission",
       size = "Coverage")

g2 <- ggplot(df_miss, aes(col, row)) +
  geom_point(aes(size = coverage_obscured, color = colorectal_cancer_screenings), alpha = 0.8) +
  geom_text(data = df_full, aes(label = state), color = "black", nudge_y = 0.05) +
  geom_text(aes(label = percent(colorectal_cancer_screenings/100, 0.01)), nudge_y = -0.1, size = 2.5) +
  theme_void() +
  scale_color_gradient2(low = "#3F6E9A", high = "#AB4C30", midpoint = median(df_miss$colorectal_cancer_screenings), mid = "#E7D9C6") +
  scale_size(range = c(3, 30)) +
  scale_y_reverse()  +
  labs(color = "Cancer Screening",
       size = "Coverage")

g1 + g2 + plot_layout(guides = "collect") 

Figure S1: This figure recreates Figure 3 in Basole et al. (2021).

theme_set(theme_classic())
g1 <- ggplot(df_miss, aes(coverage_obscured * 100, colorectal_cancer_screenings)) +
  geom_point() +
  labs(x = "Coverage (%)", y = "Cancer Screening (%)") +
  geom_smooth(method = loess, formula = y ~ x) +
  annotate("richtext", x = 80, y = 73, label.color = NA, fill = "transparent", label = glue::glue("R<sup>2</sup> = {scales::comma(cor(df_miss$coverage_obscured, df_miss$colorectal_cancer_screenings), 0.001)}")) 

g2 <- ggplot(df_miss, aes(coverage_obscured * 100, readmission_rate * 100)) +
  geom_point() +
  labs(x = "Coverage (%)", y = "Readmission (%)") +
  geom_smooth(method = loess, formula = y ~ x) +
  annotate("richtext", x = 80, y = 15.3, label.color = NA, fill = "transparent", label = glue::glue("R<sup>2</sup> = {scales::comma(cor(df_miss$coverage_obscured, df_miss$readmission_rate), 0.001)}")) 

g1 + g2 

Figure S2: This is an alternative graph design for Figure S1.

set.seed(2021)
lineup_data <- null_permute("colorectal_cancer_screenings") %>% 
  lineup(true = df_miss, n = 20, pos = 3)

plot_lineup_theirs <- ggplot(lineup_data, aes(col, row)) +
  geom_point(aes(size = coverage_obscured, color = colorectal_cancer_screenings), alpha = 0.8) +
  theme_void() + 
  scale_color_gradient2(low = "#3F6E9A", high = "#AB4C30", midpoint = median(df_miss$colorectal_cancer_screenings), mid = "#E7D9C6") +
  scale_size(range = c(1, 5)) +
  scale_y_reverse(expand = c(0.1, 0.2))  +
  guides(color = "none", size = "none") + 
  facet_wrap(~.sample, ncol = 5) +
  scale_x_continuous(expand = c(0.1, 0.1)) + 
  theme(legend.position = "bottom",
        strip.text = element_text(size = 18, margin = margin(t = 3, b = 3)),
        strip.background = element_rect(color = "black", size = 1.5))

plot_lineup_theirs

Figure S3: The lineup for the tile grid plot.

plot_lineup_ours <- ggplot(lineup_data, aes(coverage_obscured * 100, colorectal_cancer_screenings)) +
  geom_point() +  
  geom_smooth(method = loess, formula = y ~ x) +
  facet_wrap(~.sample, ncol = 5) +
  scale_x_continuous(expand = c(0.1, 0.1)) + 
  theme(legend.position = "bottom",
        strip.text = element_text(size = 18, margin = margin(t = 3, b = 3)),
        strip.background = element_rect(color = "black", size = 1.5),
        axis.text = element_blank(),
        axis.title = element_blank(),
        axis.line = element_blank(),
        axis.ticks.length = unit(0, "pt"))

plot_lineup_ours

Figure S4: The lineup for the scatter plot.

Same plots with higher associations between variables

The following are plots based on data that purposely modifies cancer screening to induce a higher association with the coverage. This higher association is induced (as shown in the code below) by rearranging data by the coverage and modifying the cancer screening percentage so that it is ordered from low to high.

df_false <- df_miss %>% 
  arrange(coverage_obscured) %>% 
  mutate(colorectal_cancer_screenings = sort(colorectal_cancer_screenings))

lineup_false_data <- null_permute("colorectal_cancer_screenings") %>% 
  lineup(true = df_false, n = 20, pos = 5)

plot_lineup_theirs %+% lineup_false_data

Figure S5: Which plot looks the most strikingly different to you?

plot_lineup_ours %+% lineup_false_data

Figure S6: The above shows a lineup for data that was purposely manipulated so that two variables have a higher association. How easy was it to spot the data plot compared to Figure S5?

Acknowledgement

We thank Basole et al. (2021) for supplying us the synthetic data to draw the above plots.

Reference

Session Information

sessioninfo::session_info()

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##  system   x86_64, darwin17.0          
##  ui       RStudio                     
##  language (EN)                        
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##  ctype    en_AU.UTF-8                 
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##  date     2021-09-19                  
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Basole, Rahul C., Elliot Bendoly, Aravind Chandrasekaran, and Kevin Linderman. 2021. “Visualization in Operations Management Research.” INFORMS Journal on Data Science (to appear).